Internet and other online advertising continues to grow in value and reach. Unlike advertising distributed over broadcast type media, Internet and other online advertising can be targeted to user inputs or other action and/or information that a website has gotten concerning one or more users (e.g., user profiles). For example, assume a user is interested in purchasing a new car and inputs the search term “Lexus” into a World Wide Web search engine. An advertising targeting and delivery engine can be programmed to deliver, in response to such input, a Lexus dealer's ad for a particular Lexus car on the dealer's lot. If the user inputs the search term “Lexus GS”, the ad targeting engine can target and deliver an ad for that specific make and model of car.
Such timely and targeted ad delivery capabilities are very useful and efficient in creating purchasing opportunities. Timely targeted advertising messages are directly relevant and responsive to motivated user requests or other user related event(s) or action(s), and therefore have a higher likelihood of purchasing success than untargeted ads. Therefore, generally speaking, an advertising client will often pay more for targeted impressions than for untargeted impressions served at random to meet quota. Ads can be targeted on a variety of factors. For example, a new or used car dealer who wishes to sell a Mustang convertible sitting on a car lot in Atlanta will pay more for an impression delivered to someone whose search criteria indicate he or she lives in Atlanta and is looking for a Mustang or a convertible. Impressions delivered in response to other types of user requests (e.g., books about cars, Christmas gifts, etc.) are less effective and therefore less valuable to an advertiser client.
Advertising is sold and marketed in a variety of ways. One common way for Internet advertising to be sold and marketed is by guaranteeing an advertising customer a certain number of targeted views/impressions for his or her ad—that is, the total number of times potential customers have seen or viewed a particular ad. This is an easy way for advertising to be priced. Many or most advertising customers are willing to pay fees based on number of “impressions” of the ad that are actually delivered to consumers. Typically, targeted impressions have more value than untargeted ones and therefore command a higher price.
Forecasting in advance the inventory of targeted impressions that will be available in any given time period based on the number of people that view a particular web site (or, for example, a particular location within a particular web site) can be difficult. Unlike broadcast advertising where the number of ads that can be delivered in a television or radio broadcasting hour can be scheduled in advance, online advertisers do not know for sure how many users will view a particular Internet site during a given time period. It is a challenge to accurately predict how many total impressions can be delivered in a week, month or other time period. Overestimating total inventory can disappoint advertising clients who expect and are willing to pay for delivery of a certain number of impressions during a given time period. Underestimating total inventory can result in lost sales opportunities.
Even more difficult is understanding how and where existing sold advertising will serve when the customer is not buying an individual unit but rather, is purchasing impressions which can be served by any combination of individual units. For example, an ad purchase is not necessarily for an ad to always show on the homepage whenever a website is first requested and viewed by a potential consumer. Rather, the purchase could be for delivery of an ad on a particular page of a web site when a consumer has requested a certain type of information (e.g., a particular type of car, some other specific type of product, etc.).
A potentially even more complex issue in predicting available online advertising inventory involves situations when a particular ad can be served to a number of different search requests. For example, using car advertising as a non-limiting example, an advertiser may wish to show a particular car ad when a user is viewing a web site or inputs a search request that specifies the make and/or model of that particular car. On the other hand, that same car ad may serve when a user inputs a different search that specifies a body style that same car belongs to (e.g., minivan, luxury vehicle, etc.). The same ad also might be served in response to a user's inquiry relating to the particular car's price range. Many other situations are possible.
These situations raise so-called “overlap” where ads targeted to different, sometimes specific entities can each be served in response to a given consumer request, but may also be served in response to completely different requests altogether. FIG. 1 is an attempt to graphically show such an overlap situation. The left-hand side of FIG. 1 shows a first set of possible user requests 50 in response to which ad 1 could be targeted and served. On the right-hand side of FIG. 1 is shown a second set of possible user requests 52 in response to which a second ad (ad 2) could be targeted and served. As can be seen in FIG. 1, there are some kinds of user requests in the first set 50 to which only the first ad (Ad 1) will be targeted and served, and there are some types of user requests in the second set 52 to which only the second ad (Ad 2) will be targeted and served. However, in the example shown, there is a subset 54 of the first set of user requests 50 that overlaps with the second set of user requests 52. This overlap region 54 indicates user requests for which either the first ad or the second ad could be targeted and served, Because of these overlap situations, an advertiser may tend to underestimate the number of impressions he can deliver in a targeted manner.
For a more concrete example, suppose a first ad is for a particular luxury sedan (e.g., Lexus LS) manufactured by Lexus, and a second ad is for a different Lexus model (e.g., Lexus GS). Suppose ad 1 targets any time the consumer inputs a search that implicates the manufacturer Lexus, and ad 2 targets any time a user inputs a search for cars that cost over $40,000. Now suppose a user requests Lexus models that cost over $40,000. In this particular example, either ad 1 or ad 2 can serve. The user's search request in this case falls within the overlap region 54.
Even though either ad 1 or ad 2 can be served in response to this particular user request, each ad may have individual goals so that the targeting engine that delivers the ads may prefer one ad over the other. Advertising targeting engines are very sophisticated and are able to make such decisions about which ads should serve based on a number of factors including priority. However, in the past it has been difficult to predict beforehand which ad or ads will serve in such overlap situations. This is especially true in view of the fact that although FIG. 1 shows the overlap between two different sets of user requests, in actual practice there may be multiple overlaps of three, four or more subsets where two, three, four or more ads could be served in response to a particular request falling within the overlap region 54. It has been difficult in the past to predict in advance with any degree of certainty what decision a real time ad targeting engine may make in real time ad targeting.
Because of these and other difficulties, it is not unusual for those who sell online advertising opportunities to underestimate the amount of advertising inventory that is available for particular ad targeting criteria. Overlap sometimes represents an opportunity to deliver additional ads (because any of multiple ads can deliver in response to requests in the overlap region), but conservative predictive approaches may consider all such requests to be reserved or taken up by one of the ads that can deliver and therefore underestimate available impression opportunities. It would therefore be desirable to interpret and simulate how ads will actually be served at a future time based on a variety of factors including for example seasonality, other “overlapping” ads in the system, priorities, impression goals and other factors. It would be desirable to use such interpretation and simulation to forecast capacity and remaining available inventory across any type of user request in order to accurately predict the number of impressions that are available for delivering targeted ads. Certain prior art approaches use data sampling as a way to managing the extremely large data volumes inherent to ad serving. Sampling is an efficient way to accomplish complex analysis in real time without the burden of attempting to process large amounts of data. In sampling, there is no attempt to analyze all available data or events. Rather, sampling relies on deriving what is assumed to be a statistically significantly subset of data/events that system designers hope will be representative of the comprehensive data set. Analysis is then carried on only on the samples—which involve much less data and can therefore be analyzed relatively efficiently in real time computation. Thus, some prior art approaches use statistical methods to estimate the probability of available ad impressions to sell. They sample against a large population of all impressions. However, sampling does not necessarily enable the subsequent ad availability calculation at a more granular targeting level. This is because random or other types of sampling of the aggregate population of historical impressions will in general inherently over sample, under-sample, or altogether “miss” samples within sub-populations of a higher granularity. Use of sampling makes the existing commercial solutions able to execute the algorithm against data in the server's memory relatively efficiently and does not require a large high performance database against which to execute the query or a large database for storage. These prior art approaches may be efficacious for publishers that do not target at a granular level, but they do not provide granular targeting. In contrast, aspects of the exemplary illustrative non-limiting technology herein can target at a more granular level accurately. The industry is moving closer and closer to granular targeting. The audience of one is the ultimate audience in any marketing effort. The exemplary illustrative non-limiting solutions herein advance the industry towards that end state.
The exemplary illustrative non-limiting technology herein provides systems and methods for determining the total capacity, amount reserved or sold and remaining availability for ad impressions for an online advertising publishing system. Calculations may be based for example on multiple factors including 1) actual historic data (avoiding the need to sample) of ad impression delivery, 2) various delivery parameters including ad targeting overlap with other ads, timing, priority and impression goals, and 3) a mathematical algorithm and calculations of the above incorporating adjustments due to seasonality and other market factors.
One exemplary illustrative non-limiting system implementation includes a database arrangement, a predictive engine, and a user interface.
In one exemplary illustrative non-limiting implementation, the database arrangement may provide a sophisticated data model tuned for performance leveraging actual past ad impression delivery, current and future orders for various capacity, combined with multiple forms of devised data which help identify where orders may overlap with one another. Additional storage may be provided for seasonal adjustments.
In an exemplary illustrative non-limiting implementation, the predictive engine may provide multiple sets of algorithms and data search strategies crossing the database and an application development language using a multi-step approach to determine the necessary capacity, booked and availability numbers.
An exemplary illustrative non-limiting user interface may provide a web-based or other application allowing entry of requests from users, and engaging the engine and database arrangement to perform desired calculations to provide output back to users.
Search techniques can provide accurate forecasting by calculating, in a predictive manner, which impressions to be delivered in the future have been taken up by ads already sold, thereby providing an accurate indication of how many impressions are left to be sold. Part of such predictive analysis provides a more accurate prediction of how many impressions are likely to be delivered based on factors such as seasonality, historical precedents as extrapolated for current traffic levels, and other factors. After forecasting how many impressions are likely to occur within a given time in the future, it is possible to subtract the impressions that have already been sold to other advertising clients. These forecastings and subtraction calculations may be performed on a highly granular level in response to particular user input criteria, response sets to be targeted or other user events or interactions. This allows advertising sales personnel to help an advertising client define a distinctive set or subset of user inputs or other events to which an ad is to be served, thereby maximizing targeting effectiveness as well as maximizing the utilization of overall number of available impressions to serve targeted ads.
Ad overlap detection is facilitated in the exemplary illustrative non-limiting implementation by configuring the real time ad targeting and delivery system to log not only impressions actually delivered, but also impressions that could have been delivered with appropriate targeting criteria (e.g., not just the impression that “won out” in the targeting process, but also the “runners up.”) The “runners up” information is used as part of “actuals” data to more accurately assess ad overlap situation.
In one exemplary illustrative non-limiting implementation, overlap situations as shown in FIG. 1 may be assessed using a multi-variable calculation including various factors or combinations of factors. In a car advertising example, such variables could include for example car make, car model, overall market demographics, car body style, age of car, type of user search, and other factors. An overall goal is to not oversell or undersell inventory that has already been sold to other advertising clients, and to optimize the targeting of remaining inventory left to sell. Such calculations enable yield management to provide pricing based on scarcity and other factors, and also provide advertising personnel with a good understanding about what inventory is left to sell. Such understanding can be provided at a very fine level of granularity commensurate with highly precise targeting of ads. Predictive calculations can be used to analyze historical distribution of ads and perform algorithms that take into account changing conditions in the future.
In more detail, an exemplary illustrative non-limiting implementation assesses overall capacity of a particular web site to deliver ads (impressions) by analyzing historical data of ads requested and served. In one exemplary illustrative non-limiting implementation, such capacity is assessed based on monthly data (e.g., 28 days, based on each day of the week for four weeks). Such historical data may be available for example from an ad serving web log that makes a record every time an ad is served. The capacity takes into account specific user queries.
The exemplary illustrative non-limiting implementation does not necessarily however simply determine the total number of impressions that may be delivered. In addition, based on such historical analysis, the exemplary illustrative non-limiting implementation can assess, using a multi-variable analysis, more detailed capacity to serve ads in response to certain user queries or types of queries or other information. This fine granularity of capacity assessment can then be used to determine how many of the forecasted number of impressions predicted to be available in the future have already been sold to existing advertising clients.
The remaining capacity assessment in the exemplary illustrative non-limiting implementation uses a predictive analysis to determine which existing ads and their targeting criteria that have already been sold have used up which impressions. The predictive analysis can take into account the overall factors described above for situations where multiple ads might be served in response to a particular user request when factors such as priority are used dynamically in real time by the ad targeting engine to finally determine which ad would likely be served in response to a particular user request. Remaining capacity calculation in the exemplary illustrative non-limiting implementation takes priority into account as well as assessing what other types of user requests a particular ad might be served to.
The exemplary illustrative non-limiting implementation may also take seasonality into account in order to accurately forecast and optimize the number of impressions that are predicted will be served. Seasonality means more than total traffic since user queries may tend to be cyclical (e.g., higher interest in convertibles in the springtime, 4WD vehicles in the winter, etc.)
In accordance with one exemplary illustrative non-limiting implementation, the forecasting algorithms do not rely on predetermined brackets or categories but rather use simulations to provide results based upon real queries that are provided by the advertising client and/or the advertising salesperson. This allows an advertising sales team working with an advertising client to optimize utilization of remaining inventory as well as improve the forecasting of which ads will be delivered in a targeted manner.
An exemplary illustrative non-limiting implementation uses one or more projection factors from historical information and provides additional weight based on seasonality. It is thus possible to very accurately book ads to a fine level of granularity without having to rely on sampling (which may often be inaccurate).
Further exemplary illustrative non-limiting features and advantages include:                forecasting based on actual data, not just sampling        agnostic as to which engine is used to serve ads        same forecasting calculations can be generalized or used irrespective of particular ad delivery and/or targeting platform        overlap calculation with forecasting based on actuals        review current state of orders booked in the system including orders that have not started serving        in one exemplary non-limiting implementation, no requirement to rely on statistical factors because the exemplary illustrative non-limiting algorithms are capable of figuring out exactly how many impressions are available (i.e., both scarce and targeted)        outputs include indication of current orders already booked in the system, reports, and guidance as to how to maximize such optimized advertising inventory for specifically targeted ads        possibility to maximize revenue by allowing sales force to sell aggressively and with confidence        user interface provides visible information about what inventory is left to sell        